Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/pooling/max_pooling3d.py: 89%

1# Copyright 2015 The TensorFlow Authors. All Rights Reserved. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14# ============================================================================== 

15"""Max pooling 3D layer.""" 

16 

17 

18import tensorflow.compat.v2 as tf 

19 

20from keras.src.layers.pooling.base_pooling3d import Pooling3D 

21 

22# isort: off 

23from tensorflow.python.util.tf_export import keras_export 

24 

25 

26@keras_export("keras.layers.MaxPooling3D", "keras.layers.MaxPool3D") 

27class MaxPooling3D(Pooling3D): 

28 """Max pooling operation for 3D data (spatial or spatio-temporal). 

29 

30 Downsamples the input along its spatial dimensions (depth, height, and 

31 width) by taking the maximum value over an input window (of size defined by 

32 `pool_size`) for each channel of the input. The window is shifted by 

33 `strides` along each dimension. 

34 

35 Args: 

36 pool_size: Tuple of 3 integers, 

37 factors by which to downscale (dim1, dim2, dim3). 

38 `(2, 2, 2)` will halve the size of the 3D input in each dimension. 

39 strides: tuple of 3 integers, or None. Strides values. 

40 padding: One of `"valid"` or `"same"` (case-insensitive). 

41 `"valid"` means no padding. `"same"` results in padding evenly to 

42 the left/right or up/down of the input such that output has the same 

43 height/width dimension as the input. 

44 data_format: A string, 

45 one of `channels_last` (default) or `channels_first`. 

46 The ordering of the dimensions in the inputs. 

47 `channels_last` corresponds to inputs with shape 

48 `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)` 

49 while `channels_first` corresponds to inputs with shape 

50 `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 

51 It defaults to the `image_data_format` value found in your 

52 Keras config file at `~/.keras/keras.json`. 

53 If you never set it, then it will be "channels_last". 

54 

55 Input shape: 

56 - If `data_format='channels_last'`: 

57 5D tensor with shape: 

58 `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)` 

59 - If `data_format='channels_first'`: 

60 5D tensor with shape: 

61 `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)` 

62 

63 Output shape: 

64 - If `data_format='channels_last'`: 

65 5D tensor with shape: 

66 `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)` 

67 - If `data_format='channels_first'`: 

68 5D tensor with shape: 

69 `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)` 

70 

71 Example: 

72 

73 ```python 

74 depth = 30 

75 height = 30 

76 width = 30 

77 input_channels = 3 

78 

79 inputs = tf.keras.Input(shape=(depth, height, width, input_channels)) 

80 layer = tf.keras.layers.MaxPooling3D(pool_size=3) 

81 outputs = layer(inputs) # Shape: (batch_size, 10, 10, 10, 3) 

82 ``` 

83 """ 

84 

85 def __init__( 

86 self, 

87 pool_size=(2, 2, 2), 

88 strides=None, 

89 padding="valid", 

90 data_format=None, 

91 **kwargs 

92 ): 

93 super().__init__( 

94 tf.nn.max_pool3d, 

95 pool_size=pool_size, 

96 strides=strides, 

97 padding=padding, 

98 data_format=data_format, 

99 **kwargs 

100 ) 

101 

102 

103# Alias 

104 

105MaxPool3D = MaxPooling3D 

106